Skill UI

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.

A comprehensive skill for managing, provisioning, and destroying GPU instances on vast.ai. It analyzes your deep learning task to determine precise hardware requirements (VRAM, GPU count, storage) and searches for cost-optimized, high-reliability GPU offers. The skill handles the entire lifecycle from initial analysis to experiment monitoring, ensuring you only rent what you need at the best possible value.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.

Provides an expert methodology to significantly reduce AI API spending (40-80%) without sacrificing user quality. The guide covers cost auditing, optimizing existing systems, and designing cost-efficient architectures using techniques like model routing, prompt caching, output length control, and prompt compression.

Helps teams treat prompts like production code by providing registry, eval, and governance workflows for versioning, testing, and promoting prompts safely across environments.

Permanently deletes the context-mode knowledge base and resets session statistics. Users can choose between a project-wide wipe (deleting all indexed content and session data) or a targeted session wipe, ideal for isolating polluted data without losing project history. Note that this action is highly destructive and irreversible.

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

A robust, production-tested pattern for coordinating multiple specialized AI agents. The orchestrator decomposes complex tasks, routes them to the most suitable specialist, prevents redundant or conflicting work (anti-duplication), and enforces mandatory quality gates (e.g., running tests, checking file diffs) to verify results before marking a task as complete. It also includes heartbeat monitoring for task progress.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.

Manage BrowserWing’s intelligent browser automation platform: install Chrome, configure LLM providers, generate AI-explored scripts, and operate the script lifecycle through HTTP APIs for autonomous exploration, execution, and troubleshooting.